"""simple docstring"""



import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
       CTRLForSequenceClassification,
       CTRLLMHeadModel,
       CTRLModel,
   )






class  snake_case__      :


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase=14    ,		lowerCamelCase=7    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=99    ,		lowerCamelCase=32    ,		lowerCamelCase=5    ,		lowerCamelCase=4    ,		lowerCamelCase=37    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=512    ,		lowerCamelCase=16    ,		lowerCamelCase=2    ,		lowerCamelCase=0.02    ,		lowerCamelCase=3    ,		lowerCamelCase=4    ,		lowerCamelCase=None    ,		):
        __a       =    parent
        __a       =    batch_size
        __a       =    seq_length
        __a       =    is_training
        __a       =    use_token_type_ids
        __a       =    use_input_mask
        __a       =    use_labels
        __a       =    use_mc_token_ids
        __a       =    vocab_size
        __a       =    hidden_size
        __a       =    num_hidden_layers
        __a       =    num_attention_heads
        __a       =    intermediate_size
        __a       =    hidden_act
        __a       =    hidden_dropout_prob
        __a       =    attention_probs_dropout_prob
        __a       =    max_position_embeddings
        __a       =    type_vocab_size
        __a       =    type_sequence_label_size
        __a       =    initializer_range
        __a       =    num_labels
        __a       =    num_choices
        __a       =    scope
        __a       =    self.vocab_size - 1


 def 						a__   ( self						):
        __a       =    ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size						)

        __a       =    None
        if self.use_input_mask:
               __a       =    random_attention_mask([self.batch_size, self.seq_length]						)

        __a       =    None
        if self.use_token_type_ids:
               __a       =    ids_tensor([self.batch_size, self.seq_length]    ,		self.type_vocab_size						)

        __a       =    None
        if self.use_mc_token_ids:
               __a       =    ids_tensor([self.batch_size, self.num_choices]    ,		self.seq_length						)

        __a       =    None
        __a       =    None
        __a       =    None
        if self.use_labels:
               __a       =    ids_tensor([self.batch_size]    ,		self.type_sequence_label_size						)
               __a       =    ids_tensor([self.batch_size, self.seq_length]    ,		self.num_labels						)
               __a       =    ids_tensor([self.batch_size]    ,		self.num_choices						)

        __a       =    self.get_config()

        __a       =    ids_tensor([self.num_hidden_layers, self.num_attention_heads]    ,		2						)

        return (
            config,
            input_ids,
            input_mask,
            head_mask,
            token_type_ids,
            mc_token_ids,
            sequence_labels,
            token_labels,
            choice_labels,
        )


 def 						a__   ( self						):
        return CTRLConfig(
            vocab_size=self.vocab_size    ,		n_embd=self.hidden_size    ,		n_layer=self.num_hidden_layers    ,		n_head=self.num_attention_heads    ,		n_positions=self.max_position_embeddings    ,		pad_token_id=self.pad_token_id    ,		)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		*lowerCamelCase						):
        __a       =    CTRLModel(config=lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()

        model(lowerCamelCase    ,		token_type_ids=lowerCamelCase    ,		head_mask=lowerCamelCase						)
        model(lowerCamelCase    ,		token_type_ids=lowerCamelCase						)
        __a       =    model(lowerCamelCase						)
        self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)						)
        self.parent.assertEqual(len(result.past_key_values						)    ,		config.n_layer						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		*lowerCamelCase						):
        __a       =    CTRLLMHeadModel(lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()

        __a       =    model(lowerCamelCase    ,		token_type_ids=lowerCamelCase    ,		labels=lowerCamelCase						)
        self.parent.assertEqual(result.loss.shape    ,		()						)
        self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.vocab_size)						)


 def 						a__   ( self						):
        __a       =    self.prepare_config_and_inputs()

        (
            (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   
        )       =    config_and_inputs

        __a       =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

        return config, inputs_dict






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		*lowerCamelCase						):
        __a       =    self.num_labels
        __a       =    CTRLForSequenceClassification(lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()
        __a       =    ids_tensor([self.batch_size]    ,		self.type_sequence_label_size						)
        __a       =    model(lowerCamelCase    ,		token_type_ids=lowerCamelCase    ,		labels=lowerCamelCase						)
        self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)						)






@require_torch
class  snake_case__      (       snake_case_,						snake_case_,						snake_case_,						unittest.TestCase       ):
 _snake_case       :					List[Any]							  =						(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
 _snake_case       :					Union[str, Any]							  =						(CTRLLMHeadModel,) if is_torch_available() else ()
 _snake_case       :					Optional[Any]							  =						(
     {
         """feature-extraction""": CTRLModel,
         """text-classification""": CTRLForSequenceClassification,
         """text-generation""": CTRLLMHeadModel,
         """zero-shot""": CTRLForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 _snake_case       :					Any							  =						True
 _snake_case       :					Optional[int]							  =						False
 _snake_case       :					List[str]							  =						False


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
               # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
               # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
               # config could not be created.
               return True

        return False


 def 						a__   ( self						):
        __a       =    CTRLModelTester(self						)
        __a       =    ConfigTester(self    ,		config_class=lowerCamelCase    ,		n_embd=37						)


 def 						a__   ( self						):
        super().tearDown()
        # clean-up as much as possible GPU memory occupied by PyTorch
        gc.collect()
        torch.cuda.empty_cache()


 def 						a__   ( self						):
        self.config_tester.run_common_tests()


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_model(*lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(*lowerCamelCase						)


 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."						)
 def 						a__   ( self						):
        pass


 @slow
 def 						a__   ( self						):
        for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a       =    CTRLModel.from_pretrained(lowerCamelCase						)
               self.assertIsNotNone(lowerCamelCase						)






 @unittest.skip("The model doesn't support left padding"						)  # and it's not used enough to be worth fixing :)
 def 						a__   ( self						):
        pass






@require_torch
class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        super().tearDown()
        # clean-up as much as possible GPU memory occupied by PyTorch
        gc.collect()
        torch.cuda.empty_cache()






 @slow
 def 						a__   ( self						):
        __a       =    CTRLLMHeadModel.from_pretrained("ctrl"						)
        model.to(lowerCamelCase						)
        __a       =    torch.tensor(
            [[11859, 0, 1611, 8]]    ,		dtype=torch.long    ,		device=lowerCamelCase						)  # Legal the president is
        __a       =    [
            11859,
            0,
            1611,
            8,
            5,
            150,
            26449,
            2,
            19,
            348,
            469,
            3,
            2595,
            48,
            20740,
            246533,
            246533,
            19,
            30,
            5,
        ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

        __a       =    model.generate(lowerCamelCase    ,		do_sample=lowerCamelCase						)
        self.assertListEqual(output_ids[0].tolist()    ,		lowerCamelCase						)

"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


SCREAMING_SNAKE_CASE__:int									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        warnings.warn(
            "The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
            " Please use DeformableDetrImageProcessor instead."    ,		lowerCamelCase    ,		)
        super().__init__(*lowerCamelCase    ,		**lowerCamelCase						)
